The monitoring of indoor environments as well as resources (e.g. the use of appliances) has recently gained importance as energy efficiency has become crucial to cost savings, and technological advances in a variety of areas of endeavor have made it possible to gather and process relevant data in real time. However, despite these technological advances, which we describe in some detail below, there are a host of hurdles (e.g. issues of data privacy and scalability) that have not yet been sufficiently addressed by the state of the art.
For a variety of reasons, indoor as well as outdoor lighting is presently moving away from traditional fluorescent, HID, and incandescent lamps, and towards digital lighting technologies incorporating semiconductor light sources (e.g. light-emitting diodes (LEDs)). LED lighting offers, among other benefits, the advantages of controllability, high energy conversion and optical efficiency, durability, and significantly lower operating costs. Additionally, more recent advances in controllable LED technology has resulted in a variety of efficient and robust full-spectrum lighting sources that can be easily controlled to create various lighting effects and scenes for use in theatrical, office or home settings.
As in lighting technologies, rapid developments have been made in the area of sensor technologies as well. As a result of these improvements, sensors are becoming smaller, cheaper and therefore more ubiquitous. As they now fit into various everyday devices (e.g. light source housings, cameras, and various other small appliances), they are not only being used to continuously measure and monitor simpler environmental metrics (e.g. natural illumination, noise, occupancy, temperature, humidity) but also more complex metrics such as resource and space usage, activity levels, intrusion, and mood, which may require the coordinated use of a various of sensor types.
Further, the intersections of innovation in the realms of wireless communications, smart mobile devices, and cloud computing have created even more possibilities for smart environments. With a new generation of computing devices with unparalleled mobility and computational power, connected appliances (e.g. Internet of Things), access to ubiquitous connected sensors, and the processing and analytic power of cloud-based services, we are now able to gather and process data from both our immediate and remote environments in near real time. As more devices are able to not only connect to a public or private network (e.g. the Internet), but also to connect and communicate with each other, there has been a proliferation of data regarding our physical environment. We now know when devices have broken or are close to reaching their end of life, as well as a host of information about the environmental conditions surrounding these devices owing to the proliferation and integration of sensors within many types of devices. As such data is generated round the clock, there is the ever increasing need to analyze it in a timely and meaningful way such that unfavorable conditions may be rectified or disaster avoided. However, as discussed below, there are many pitfalls that need to be avoided in the quest to monitor and manipulate conditions within these smart physical spaces.
As sensors gather data from all corners of a physical building, including rooms associated with private persons or groups of people, the data gathered may be sensitive image data or data from smart phones or tablets capable of identifying individuals and the activities they are engaged in. The gathering and processing of such data gives rise to issues of data privacy, which may become central to the proper functioning of a system for monitoring physical spaces. How such data needs to be anonymized and/or aggregated, who gets to view the raw or analyzed data, how the data is presented to avoid identifying particular individuals, how the data is securely transmitted from its source (e.g. sensors that gather the raw data) to its destination (e.g. a cloud-based server or an app on an individual's smart phone) needs to be carefully considered and implemented. Existing systems for monitoring space and resource usage have not developed comprehensive strategies to address such issues.
Additionally, space and resource usage data gathered from physical spaces needs to be accurately time-stamped, gathered with sufficient granularity, and processed in a timely manner in order to provide insights that may result in energy or other usage efficiencies. For example, occupancy data that isn't appropriately time-stamped may not provide accurate information regarding when spaces such as meeting rooms or corridors are heavily used. And delays in providing this data to an appropriate cloud server for analysis may, for example, lead to the failure to timely schedule maintenance in high traffic areas or positively affect the scheduling of such spaces in the near future (e.g. by suggesting alternative corridors to direct flow of traffic). Again, existing systems for monitoring space and resource usage have not developed comprehensive strategies to address such issues.
In order to adequately manage the energy and/or space usage at a large site, such as a building that serves as the headquarters of a large corporation, a large number (and variety) of sensor devices would typically be required to perform the basic data generation. Moreover, such facilities typically have a wide variety of appliances (e.g. printers, scanners, lighting devices), and monitoring their usage would also provide useful information regarding energy efficiency. While these devices may all be able to connect to a network such as a local area network, the data they generate will likely be in a variety of formats. Accordingly, any system that is to use the information generated by all the devices present needs to be able to convert between the formats of data coming from these devices to one or more common formats that can be understood and processed by various data processing applications used by the system. At the same time, such building-wide systems need to make it easy for a variety of devices to interface with each other and with the system (e.g. by exposing APIs that makes it easy to add support for new communications protocols or new types of devices). And even as such building or site-wide systems for monitoring spaces and resources grow in their functional complexity, installing and upgrading individual components within such systems need to be relatively seamless, without the need for the involvement of highly skilled installers every step of the way.
Moreover such systems need to have the capability to incorporate new devices without overwhelming any portion of the system. In other words, such systems need to be scalable in that they should be able to comfortably accommodate a growing number of devices and users, and consequently a growing amount of data generated, from a network bandwidth, installation efficiency, and data analysis standpoint. And lastly such complex systems need to be able to manage errors without significant data loss. In other words, they need to anticipate and minimize loss of data when hardware or software fails.
No existing system for monitoring and controlling physical environments provides solutions to at least the aforementioned challenges. The systems, methods and apparatuses presented below provide solutions designed to address these and other challenges.